class_docstr: Invertible `TextEncoder` using word pieces with a byte-level fallback.
  
  Encoding is fully invertible because all out-of-vocab wordpieces are
  byte-encoded.
  
  The vocabulary is "trained" on a corpus and all wordpieces are stored in a
  vocabulary file. To generate a vocabulary from a corpus, use
  `tfds.deprecated.text.SubwordTextEncoder.build_from_corpus`.
  
  Typical usage:
  
  ```
  # Build
  encoder = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(
      corpus_generator, target_vocab_size=2**15)
  encoder.save_to_file(vocab_fname)
  
  # Load
  encoder = tfds.deprecated.text.SubwordTextEncoder.load_from_file(vocab_fname)
  ids = encoder.encode("hello world")
  text = encoder.decode([1, 2, 3, 4])
  ```
docstr: Extracts list of subwords from file.
docstr_tok: ["Extracts","list","of","subwords","from","file","."]
code: def load_from_file(cls, filename_prefix):
      """Extracts list of subwords from file."""
      filename = cls._filename(filename_prefix)
      lines, _ = cls._read_lines_from_file(filename)
      # Strip wrapping single quotes
      vocab_list = [line[1:-1] for line in lines]
      return cls(vocab_list=vocab_list)
  
imports: from __future__ import unicode_literals
  import collections
  from absl import logging
  from tensorflow_datasets.core.deprecated.text import text_encoder
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 13 for calling the SubwordTextEncoder obj's underlying member method code with example usage: obj.load_from_file(cls, filename_prefix) and returns: cls